|Publication number||US7769778 B2|
|Application number||US 11/819,905|
|Publication date||Aug 3, 2010|
|Filing date||Jun 29, 2007|
|Priority date||Jun 29, 2007|
|Also published as||US20090006394|
|Publication number||11819905, 819905, US 7769778 B2, US 7769778B2, US-B2-7769778, US7769778 B2, US7769778B2|
|Inventors||Robert F. Snapp, James Daniel Self|
|Original Assignee||United States Postal Service|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (17), Non-Patent Citations (2), Referenced by (7), Classifications (19), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This invention generally relates to character string validation and error correction and, more specifically, to selecting the best matching word for an incorrectly spelled word, such as a misspelled street name in an address.
In a computer or other data processing system, text is typically processed using a standard encoding scheme (e.g., ASCII or Unicode) to represent each of the individual characters (e.g., a letter or a number) in a word or a number. An entire word or number, or group of words or numbers, is typically represented by a set or string of characters in a standard encoding scheme.
In an item delivery environment, character strings are employed to represent information related to items that need to be delivered, such as a piece of mail or a package. In particular, a delivery address indicating the location to which an item is to be delivered may be represented by a character string, or set of character strings. The delivery address may come from various sources: it may be read from the surface of a delivery item by an OCR system; it may come from an electronic mailing list; it may be scanned in from a paper mailing list; etc.
Regardless of the source, a word or number, and the equivalent computer representation, may have an error in it. Errors may be in the form of misspellings, typographical errors, incorrect information, incorrect words, transposed numbers, misread characters, etc. Such errors are often introduced when a word or number is entered into a computer file by a human typist, optical character recognition system, scantron reader, speech recognition system, etc.
Depending upon the end use of the computer representation of the word or number, it may be important to correct such errors. For example, delivery services strive to correct errors in the words and numbers of an address because it is very costly to return a delivery item to a sender, and returned items cause sender dissatisfaction. Yet, to deliver an item, a governmental delivery service, such as the U.S. Postal Service® (USPS®), is legally required to determine with a specified minimum degree of certainty that the digital representation of the address used to direct the delivery of an item is the valid and intended address for delivery. Other delivery services may have similar commercial requirements, because, in general, all delivery services strive to avoid delivering items to the wrong address or returning items to the sender.
Address information may be used for other purposes that require low error rates in address validation and correction processes, in addition to directing items for delivery. For example, the USPS® uses address information to determine whether a customer has filed a change-of-address (“COA”) order with the USPS® and to automatically forward a delivery item to a customer's new address when appropriate. Other delivery services may have similar systems and abilities. Other application areas, such as medical services, security services, and financial services, to name a few, also benefit from address information correction and require a high degree of certainty that the words and numbers in a digital representation, such as a character string, are the valid and intended interpretations, and that any corrections are accurate.
One example of a source of addresses that require validation and correction is a mailing list. Organizations typically use mailing lists containing the names and addresses of individuals interested in the organizations' products or services to send material to multiple recipients. Such mailing lists are typically kept in a computer-readable form, such as a text file or a database file. An organization may provide a mailing list to a delivery service, such as the U.S. Postal Service, for use in sending, for example, newsletters, periodicals, or advertising to the individuals on the mailing list. Organizations wish to avoid wasting materials and money by sending material to invalid or incorrect addresses contained in their mailing list.
It is worth noting that accurate mailing lists are valuable in their own right. For some organizations, such as specialized niche publications or charitable groups, their mailing lists may be revenue-generating assets. There are even mailing list brokers that help organizations maximize the value of their mailing lists by renting or selling them. The value of a mailing list is enhanced when the addresses on it are valid and error-free.
Accordingly, it is desirable to develop systems and methods that recognize errors in digital representations of address information, and accurately correct such errors. For many applications, it is also desirable to validate and correct address information in a speedy manner.
Embodiments consistent with the present invention include systems, methods, and software for validating an address comprising operations and/or apparatus for identifying a set of street name character strings corresponding to the streets in a defined geographic focal locale; organizing the set of street name character strings into a fast search table; receiving an input address string containing an input street name character string field and an input building number string field, wherein the input address string represents a location within the defined geographic focal locale; searching the fast search table for a matching street name character string that exactly matches the input street name character string field; if an exactly matching string is not found, determining the matching street name character string from the fast search table to be a street name character string that most closely matches the input street name character string field; accessing, according to the matching street name character string, a single address record from a plurality of address records in a comprehensive address data set, wherein the single address record includes a number range; calculating whether the input building number string field represents a number that is within the number range; and if the input building number string field represents a number that is within the number range, outputting an indication that the input address string is valid.
Further embodiments consistent with the present invention include systems, methods, and software for validating an address using operations and apparatus for receiving an input address having an input street name field and an input building number field, wherein the input address represents a location within a defined geographic area; searching a fast search table corresponding to the defined geographic area for a matching street name that exactly matches the input street name field, wherein the fast search table comprises representations of streets in the defined geographic area; if an exactly matching street name is not found, assigning the matching street name to be a street name from the fast search table that exceeds a predetermined threshold of similarity to the input street name field; accessing a number range from an address data record corresponding to the matching street name, wherein the address data record is one among a plurality of address data records; calculating whether the input building number field represents a number that is encompassed by the number range; and if the input building number field represents a number that is encompassed by the number range, outputting an indication that the input address is valid.
Advantages and aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention and together with the description, serve to explain the principles of the invention.
Reference will now be made in detail to exemplary embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever convenient, the same reference numbers will be used throughout the drawings to refer to the same or like parts.
The USPS® has developed systems and techniques to recognize and correct errors in the computer or digital representations of words and numbers, including the words or numbers in an address used by the USPS® for directing the delivery of items. Other delivery services may have similar systems. One USPS® system in this area is called the address matching engine or ZIP+4® engine, which is a computer application that uses an address data set listing all cities and streets organized by delivery area and including the ranges of street numbers for the buildings that the USPS® delivers to along those streets. Certain embodiments of the ZIP+4® engine are described in U.S. Pat. No. 7,031,959, which is hereby incorporated herein by reference.
In operation, the ZIP+4® engine accepts an input address, such as “123 Main, Greatbend, KS,” and first produces a corresponding digital representation, such as a character string, in a standardized format, such as “123 MAIN ST, GREAT BEND, KS 67532-1439.” After converting the input address to a standardized form, the ZIP+4® engine evaluates the standardized street name field (e.g., “MAIN ST”) and the building number field (e.g., “123”) to verify that there is an actual street name in the delivery area specified by the address (e.g., an area that encompasses ZIP Code™ “67532”) that matches the input street name, and if so, that the street number is within the range of valid building numbers for that matching street. The ZIP+4® engine typically evaluates an area larger than the ZIP Code™ area identified in the input address, including, as explained below, a USPS® finance number area. As mentioned, to perform this evaluation, the ZIP+4® engine uses a predetermined, address information data set maintained by the USPS® for each delivery area.
As shown, the data set also includes the Soundex code representation (column 110) (explained below) of each standardized street name (column 115), and may contain other information (represented by 125) that is not important to this explanation. There may be additional rows or records 127 included in the data set. In one embodiment, the data set may be stored on a computer-readable medium for access by a computer application, such as the ZIP+4® engine.
The Soundex phonetic algorithm was developed by Robert Russell and Margaret Odell and is described in U.S. Pat. No. 1,261,167 and U.S. Pat. No. 1,435,663, which are hereby incorporated herein by reference. As illustrated by rows 135 in the data table of
More specifically, the phonetic algorithm used in the ZIP+4® engine executes the following steps: (1) preserve the first character of the street name (e.g., the “M” from “Main”); (2) condense the street name by eliminating embedded spaces and repeated consonants (e.g., “East Main” becomes “EastMain”); and (3) assign each remaining consonant in the condensed word a numeric code according to the phonetic rules of the algorithm, until the end of the word is reached or until six codes have been assigned. The ZIP+4® engine uses the following Soundex-based phonetic rules in its algorithm:
Assign a 0 to each “S” and “Z”;
Assign a 1 to each “B” and “P”;
Assign a 2 to each “M” and “N”;
Assign a 3 to each “R”;
Assign a 4 to each “D” and “T”;
Assign a 5 to each “F” and “V”;
Assign a 6 to each “J”;
If a “C” is followed by an “E”, “I”, or “Y”, assign it a 0 (like “S”
above); otherwise, assign it a 7;
If a “G” is followed by an “E”, “I”, or “Y”, assign it a 6 (like “J”
above); otherwise, assign it an 7 (like hard “C” above);
Assign a 7 (like hard “C” and hard “G” above) to each “K” and “Q”;
Assign an 8 to each “L”.
This set of rules yields representations for examples of street names as shown in column 110 of the data set shown in
The type of phonetic algorithm used in the ZIP+4® engine™ is said to be “left-weighted,” which means the matching logic assumes that the first characters of the input word are spelled correctly. For example, this type of algorithm produces the same phonetic code for the words “MAIN” and “MAINE,” but very different phonetic codes for “MAIN” (M200000) and “EMAIN” (E220000) Thus, if the error in an input word involves the first character, a left-weighted algorithm will produce a phonetic code that will differ greatly from the correctly spelled word and would not consider the words to be a fuzzy match.
Another shortcoming of phonetic algorithms is their inability to match words having single-letter errors that produce phonetic codes that differ from the correctly spelled version of the word—in other words, minor spelling errors that change the English pronunciation of a word. Table 1 shows examples of how a single incorrect vowel can alter the pronunciation of a word, which will dramatically change the phonetic code for the word produced by a phonetic algorithm:
hard “G” as in “golf” before “U”
soft “G” as in “gentle” before silent “E”
hard “C” as in “care” before “U”
sibilant C as in “certify” before silent “E”
As shown in Table 1, a Soundex phonetic algorithm would not consider the misspelled street name “Cedar Ridgu” to be a close match for the correctly spelled “Cedar Ridge” and would not consider the misspelled street name “Terracu” to be a close match for the correctly spelled “Terrace.”
Yet another shortcoming of phonetic matching algorithms is that they cannot be applied to numbers, such as a ZIP Code™ delivery code.
As noted above, for each street name in the data table that phonetically matches the input street name, the ZIP+4® engine™ performs address range check processing using number range data 120 from the data set. For the input address “123 MAIN ST, GREAT BEND, KS 67532-1439” example, the ZIP+4® engine would access the data set and evaluate whether the building number “123” is within the street number range 147 for Main Street, (and determine that it is not, because the range 147 is from “400-499”), and then perform similar accesses and evaluations for “Mine,” “Maine,” and “Mane.” Multiple iterations of the address range check processing are time consuming and inefficient. Embodiments consistent with the principals of the invention solve Many of the shortcomings of the ZIP+4® engine.
The focal locale may be any defined geographic area. In one embodiment, the focal locale is larger than a single ZIP Code™ area, because although it may not be wise to make the focal locale too large, (which may result in many duplicate street names or similar spelled street names within nearby towns and cities), it increases efficiency to make the scope of the focal locale large enough to capture cases where the address the sender intended can be matched to an address in the surrounding geographic area, even if is not in the exact town or city specified in the input address. The focal locale may be determined by any criteria. For example, for fiscal accounting purposes, the USPS® assigns a “finance number” to groups of delivery areas across the country, where each delivery area in the group corresponding to a given finance number may include several cities, several ZIP Codes™ areas, and possibly span across more than one state. In one embodiment, the finance number associated with the city, state, and/or ZIP Code™ delivery code of an address is considered the focal locale for that address. In other embodiments, the focal locale may be an area encompassed by a group of contiguous ZIP Code™ areas, city, county, state, or other political subdivision.
One of ordinary skill will recognize that an address information data set may contain more or less information than shown in
Referring again to
As noted above, the focal locale 305 may correspond to a finance number geographic area, or other geographic area. In one embodiment, fast search tables are constructed for all focal locales of interest, for instance, all the USPS® finance number geographic areas in the United States.
With a fast search table(s) ready for use, the process receives an input address and determines the focal locale corresponding to the address (stage 215), as shown in
At stage 215, the process may determine the focal locale corresponding to the input address based on the city name field 330, the state name field 335, and/or the ZIP Code™ field 340, or any combination of these fields. For example, the USPS® maintains a database of ZIP Code™ delivery codes belonging to each finance number. In a USPS® example, the process my determine the finance number focal locale by looking up the finance number corresponding to the ZIP Code™ delivery code in the ZIP Code™ field 340 of the input address. Using the input address shown in
Next, the process performs a search of the appropriate fast search table for the focal locale to determine whether the fast search table contains a street name matching the street name field 325 of the input address (stage 220). Continuing with the example, the process may perform a binary search of table 300 (
At stage 225, the process determines whether there is an exact match for the input street name in the fast search table. If so (stage 225, yes), then the process branches to stage 240. Otherwise (stage 225, no), the process branches to stage 230. A rapid search in stage 220 may be advantageous in embodiments that process a large number of input addresses, such as might come from a mailing list, because time saved quickly finding exact matches (stage 225, yes) may offset time spent searching for non-exact matches (stage 225, no).
At stage 240, for the case where there is an exact match for the input street name in the fast search table, the process uses the matching input street name to access information about the street in the comprehensive address data set, such as the data set 150 shown in
At stage 230, for the case where there is no exact match for the input street name in the fast search table, (as is the case in our example where the input street name “Marne” does not exactly match any of the street names 310 in fast search table 300 for focal local “02”), the process compares the input street name to street names in the fast search table 300 for the focal locale determined from the input address. In one embodiment, the process compares the input street name to every street name in the fast search table 300. As a product of this comparison, stage 230 creates a ranked list of the street names from the fast search table organized in order of the degree to which each street name from the fast search table matches the input street name.
In one implementation consistent with the invention, when processing digital representations of words, stage 230 uses a non-phonetic matching algorithm to determine fuzzy matches by measuring the similarity between two words. A distance algorithm is an example of a non-phonetic algorithm, and the Levenshtein Distance algorithm is a well known example of a distance algorithm. Other types of non-phonetic algorithms, such as those that measure string metrics or edit distances, (e.g., the Hamming distance algorithm), may also be used to measure the similarity between two words or numbers. Some implementations of distance algorithms output a similarity percentage figure (0-100%) after comparing two character strings, which can be used to create a ranked list of fuzzy matches.
Distance algorithms typically give no significance to character position. For example, the following four pairs of strings each demonstrate a 75% similarity (or expressed differently, a distance of 1) according to the Levenshtein distance algorithm: “WORK” and “FORK,” “WORK” and “WORD,” “TIME” and “TILE,” and “TIME” and “TAME.”
In our input street name example from
Using a non-phonetic character string matching algorithm and keeping track of the similarity of the input street name to the street names from the fast search table 300, stage 230 produces a ranked list 360 of street names 350, as shown in
A phonetic algorithm, such as the Soundex algorithm described previously, is not suitable for use in stage 230 because it cannot produce an indication of the degree of similarity between two character strings, and thus cannot be used to create a ranked list or determine which character string is most similar to an input character string.
Referring again to
Next, the process uses information from the comprehensive address data set 150 to determine whether the building number from the input address is within the number range for the matching street name (stage 245). If the building number is within the number range for the street (stage 245, yes), then the process branches to stage 250. Otherwise (stage 245, no), the process branches to stage 255. In stage 255, the process outputs an indication that the input address was not found in the focal locale, and ends. In stage 250, the process outputs an indication that the input address is valid, and ends. In some embodiments, the output of stage 250 includes the correctly spelled matching street name or the entire corrected input address.
Continuing with the example, at stage 245 the building number in the number field 320 from the input address “99 Marne St, Great Bend, KS 67532” is “99,” and the matching street name from stage 235 is “Maine.” As shown in the comprehensive address data set 150 in
One of ordinary skill will recognize that other systems and processes may receive the output of stages 250 and/or stage 255, and perform their operations according to this output. For example; a mailing list may be updated with corrected street names and/or addresses based on the output of stage 250 so that the mailing list contains only corrected addresses; a mailing list may be updated to delete invalid addresses based on the output of stage 255 so that the mailing list contains only valid addresses; a package may be returned to the sender based on the output of stage 255; the focal locale may be expanded and the process run again based on the output of stage 255; the input address may be provided to a human operator for further analysis based on the output of stage 255; or for embodiments that output the address with a corrected street name from stage 250, the corrected address may be verified by a separate system, such as the USPS®'s DPV™ system, which accepts an input address and confirms that at least one delivery has been previously made to that delivery point address.
One of ordinary skill will further recognize that the process of
For another example of a modification to the process, stage 235 may be modified to choose one street name as being the highest ranked when the matching algorithm outputs two or more equally ranked choices. The determination of a choice may be based on other information from the input address, such as the building number or the name of the person or business associated with the input address, analyzed in light of the information in the comprehensive address data set 150 or other related data sets. Stage 235 may assign the highest ranking to one street name over another based on this additional analysis. Alternatively, stage 235 may be modified to notify a human operator when the matching algorithm outputs two or more equally ranked choices, and the operator may assign one of the choices the highest ranking after investigating tie-breaking criteria. In yet another alternative, stage 235 may be modified to output two or more equally ranked choices with an indication that they are tied, and stages 240, 245, and 250 may be modified to perform a building number range check on each of the tied, equally ranked choices, and if only one passes the range check, output the passing one as the valid street name within the focal locale. In yet another alternative, a stage may be added after stage 235 to invoke stage 255 in the case where ranked list is empty, or where none of the fuzzy matches in the ranked list exceeds a minimum threshold of similarity to the input street name.
In another example of a process modification, stages may be added such that when an exactly matching input street name fails the number range test (stage 245, no), the exactly matching street name is then treated as a non-exactly matching street name and provided as input to stage 230.
For yet another example, similar variations of the illustrated process could be applied to fields of an address other than the street name field 325, such as the city name field 330 or the ZIP Code™ field 340. For instance, in one embodiment of a ZIP Code™ character string implementation, the process may attempt to match an input ZIP Code™ delivery code to a table of ZIP Codes™ encompassed by a focal locale determined by the city 330 and state 335 fields of the input address.
One of ordinary skill will also recognize that although the exemplary process shown in
Computing system 400 includes a number of components, such as a central processing unit (CPU) 410, a memory 420, an input/output (I/O) device(s) 430, and a database 460. System 400 that can be implemented in various ways. For example, an integrated platform (such as a workstation, personal computer, laptop, etc.) may comprise CPU 410, memory 420 and I/O devices 430. In such a configuration, components 410, 420, and 430 may connect through a local bus interface and access database 460 (implemented as a separate database platform). The access connection may be implemented through a direct communication link, a local area network (LAN), a wide area network (WAN) and/or other suitable connections. System 400 may be standalone or it may be part of a subsystem, which may, in turn, be part of a larger system, such as an OCR system, sorting system, mailing list maintenance system, inventory system, employee records system, financial records system or document processing system.
CPU 410 may be one or more known processing devices, such as a microprocessor from the Pentium™ family manufactured by Intel™. Memory 420 may be one or more storage devices configured to store information accessed, read, and/or used by CPU 410 to perform certain functions and processes related to embodiments of the present invention. Memory 420 may be a volatile or non-volatile, magnetic, semiconductor, tape, optical, or other type of storage device or computer-readable medium. In one embodiment consistent with the invention, memory 420 includes one or more application programs or subprograms 425 that, when executed by CPU 410, perform various methods or processes consistent with the present invention. For example, memory 420 may include a correction program 425 that validates or corrects a digital representation, such as a character string, of a word or number, such as the street name character string from an input address character string, or memory 420 may include a comparison program 425 implementing a process that searches for valid digital representations of a word that match an input word, or memory 420 may include an analysis application program 425 that analyzes information related to the information in a character string for use in determining the correctness of, and if necessary correcting, the character string. Memory 420 may also include other programs that perform other functions and processes, such as programs that maintain electronic mailing lists and programs that perform delivery point verification of a standardized address character string. The programs in memory 420 may communicate with each other.
Methods, systems, and articles of manufacture consistent with the present invention are not limited to programs or computers configured to perform dedicated tasks. For example, memory 420 may be configured with a program 425 that performs several functions when executed by CPU 410. That is, memory 420 may include a program 425 that performs database information extraction functions, search table construction functions, character recognition functions, digital representation (such as a character string) matching functions, character string substitution or correction functions, and machine control functions. Alternatively, CPU 410 may execute one or more programs located remotely from system 400. For example, system 400 may access one or more remote programs that, when executed, perform functions related to embodiments of the present invention.
Memory 420 may be also be configured with an operating system (not shown) that performs several functions well known in the art when executed by CPU 410. By way of example, the operating system may be Microsoft Windows™, Unix™, Linux™, an Apple Computers operating system, Personal Digital Assistant operating system such as Microsoft CE™, or other operating system. The choice of operating system, and even to the use of an operating system, is not critical to the invention.
I/O device(s) 430 may comprise one or more input/output devices that allow data to be received and/or transmitted by system 400. For example, I/O device 430 may include one or more input devices, such as a keyboard, touch screen, mouse, and the like, that enable data to be input from a user. Further, I/O device 430 may include one or more output devices, such as a display screen, CRT monitor,. LCD monitor, plasma display, printer, speaker devices, and the like, that enable data to be output or presented to a user. I/O device 430 may also include one or more digital and/or analog communication input/output devices that allow computing system 400 to communicate with other machines and devices, including control communications. The configuration and number of input and/or output devices incorporated in I/O device 430 are not critical to the invention.
Database 460 may comprise one or more databases that store information and are accessed and/or managed through system 400. By way of example, database 460 may be an Oracle™ database, a Sybase™ database, or other relational database. Database 460 may include, for example, tables or lists of valid digital representations, such as character strings, of address information, such as street name character strings, address information data sets, databases of address fields cross-referenced to other related address fields, geographic data, delivery point data, employee data, governmental data, etc. Systems and methods of the present invention, however, are not limited to separate databases or even to the use of a database, as data can come from practically any source, such as the internet and other organized collections of data.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
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|U.S. Classification||707/780, 707/999.102, 707/999.006, 707/999.005, 382/229, 382/101, 707/999.101, 707/999.001|
|International Classification||G06F7/00, G06F17/30|
|Cooperative Classification||G06F17/30542, Y10S707/99935, Y10S707/99942, Y10S707/99931, Y10S707/99943, Y10S707/99936, G06F17/30595|
|European Classification||G06F17/30S8R, G06F17/30S4P8F|
|Oct 16, 2007||AS||Assignment|
Owner name: UNITED STATES POSTAL SERVICE, DISTRICT OF COLUMBIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SNAPP, ROBERT F;SELF, JAMES DANIEL;REEL/FRAME:019989/0755;SIGNING DATES FROM 20070924 TO 20071003
Owner name: UNITED STATES POSTAL SERVICE, DISTRICT OF COLUMBIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SNAPP, ROBERT F;SELF, JAMES DANIEL;SIGNING DATES FROM 20070924 TO 20071003;REEL/FRAME:019989/0755
|Nov 16, 2010||CC||Certificate of correction|
|Feb 3, 2014||FPAY||Fee payment|
Year of fee payment: 4